Generate E-commerce Product Background by Integrating Category Commonality and Personalized Style

• Published in: arXiv.org 2023-12
• Main Authors: Wang, Haohan, Feng, Wei, Lu, Yang, Li, Yaoyu, Zhang, Zheng, Lv, Jingjing, Zhu, Xin, Shen, Junjie, Lin, Zhangang, Lixing Bo, Shao, Jingping
• Format: Article
• Language: English
• Subjects: Commonality; Customization; Electronic commerce
• ISSN: 2331-8422

The state-of-the-art methods for e-commerce product background generation suffer from the inefficiency of designing product-wise prompts when scaling up the production, as well as the ineffectiveness of describing fine-grained styles when customizing personalized backgrounds for some specific brands. To address these obstacles, we integrate the category commonality and personalized style into diffusion models. Concretely, we propose a Category-Wise Generator to enable large-scale background generation for the first time. A unique identifier in the prompt is assigned to each category, whose attention is located on the background by a mask-guided cross attention layer to learn the category-wise style. Furthermore, for products with specific and fine-grained requirements in layout, elements, etc, a Personality-Wise Generator is devised to learn such personalized style directly from a reference image to resolve textual ambiguities, and is trained in a self-supervised manner for more efficient training data usage. To advance research in this field, the first large-scale e-commerce product background generation dataset BG60k is constructed, which covers more than 60k product images from over 2k categories. Experiments demonstrate that our method could generate high-quality backgrounds for different categories, and maintain the personalized background style of reference images. The link to BG60k and codes will be available soon.